A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies

Abstract

Recently, five Global LAnd Surface Satellite (GLASS) products have been released: leaf area index (LAI), shortwave broadband albedo, longwave broadband emissivity, incident short radiation, and photosynthetically active radiation (PAR). The first three products cover the years 1982–2012 (LAI) and 1981–2010 (albedo and emissivity) at 1–5 km and 8-day resolutions, and the last two radiation products span the period 2008–2010 at 5 km and 3-h resolutions. These products have been evaluated and validated, and the preliminary results indicate that they are of higher quality and accuracy than the existing products. In particular, the first three products have much longer time series, and are therefore highly suitable for various environmental studies. This paper outlines the algorithms, product characteristics, preliminary validation results, potential applications and some examples of initial analysis of these products.

Keywords:

• earth observation
• global environmental change
• remote sensing

Acknowledgements

This study was supported by the ‘Generation and Application of Global Products of Essential Land Variables’ project funded and managed by the National Remote Sensing Center of China, Ministry of Science and Technology of China (2009AA122100) with the participation of about 20 universities and research institutes. Prof. Guanhua Xu, Academician of the Chinese Academy of Sciences and former Minister of the Ministry of Science and Technology of China, provided consistent support and advice. Without his encouragement and guidance, this project would not have been possible. We also are grateful for the support and guidance from many of our colleagues and other experts in the field. In particular, we would like to thank Dr Eric Vermote for his assistance in acquiring and processing the NASA 0.05° long-term AVHRR data-set, and Dr Suguan Liu for acquiring the MODIS data.

The GLASS products are available from the Beijing Normal University Center for Global Change data Processing and Analysis at http://www.bnu-datacenter.com and also available from the University of Maryland Global Land Cover Facility at http://glcf.umd.edu.